{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:43:06Z","timestamp":1791898986357,"version":"4.3.4"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[1993,1,1]],"date-time":"1993-01-01T00:00:00Z","timestamp":725846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1993,1,1]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:p>Statisticians commonly make causal inferences from the results of randomized experiments, but usually question causal inferences from observational studies on the grounds that untestable assumptions are required. This paper explains the basis for this situation and examines, within quite a general framework, various assumptions that appear in the literature. It is demonstrated how the role of each assumption is intimately related to the sort of causal inference being considered. Contrary to general belief, it is shown that neither \u2018no confounding\u2019 nor \u2018randomization\u2019 are sufficient assumptions for testing the fundamental causal hypothesis of \u2018no causation\u2019. They become sufficient, however, if supplemented by a \u2018modelling\u2019 assumption for how unobserved covariates affect potential responses. Without supplementary assumptions, no confounding and randomization allow testing of weaker but more important hypotheses, such as \u2018no mean effect\u2019. This follows from the sufficiency of an assumption of \u2018ignorable treatment assignment\u2019 for testing the hypothesis of \u2018no distribution effect\u2018. It is argued that these ideas will have some operational relevance if we can quantify our beliefs about how closely assumptions hold. to illustrate, a novel form of sensitivity analysis is contrasted with an analysis presented by Rosenbaum and Rubin.<\/jats:p>","DOI":"10.1111\/j.2517-6161.1993.tb01915.x","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T01:54:53Z","timestamp":1544147693000},"page":"455-466","source":"Crossref","is-referenced-by-count":60,"title":["The Assumptions on Which Causal Inferences Rest"],"prefix":"10.1093","volume":"55","author":[{"given":"Richard","family":"Stone","sequence":"first","affiliation":[{"name":"CSIRO Division of Mathematics and Statistics , Melbourne , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"2024090922045916500_rssb01915-cit-0001","first-page":"173","article-title":"Smoking and lung cancer: recent evidence and a discussion of some questions","volume":"2","author":"Cornfield","year":"1959","journal-title":"J. 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